National Institute of Technology, Kurukshetra

Prerequisites:
No prerequisites for this course

Course Nature: TC

Objectives:

Optimization algorithms have been an essential tool in many scientific and engineering fields, particularly in data science where large amounts of data need to be processed quickly.

This course aims to introduce students to new/old optimization algorithms and understand which algorithm is good for solving what kind of data science problems.

Similar Courses:

Content

Outcomes

Recognize and formulate convex, nonconvex, and structured optimization problems
Understand the basic theory and algorithms for solving large-scale problems
Gain some programming experience on numerical implementations
Use the material in current or future research

Reference Books

Optimization for Data Analysis. Stephen J. Wright and Benjamin Recht. Cambridge University Press, 2022.
First-Order Methods in Optimization. Amir Beck. SIAM, 2017.
Introduction to Optimization. Boris Polyak. Optimization software, 1987.
Lectures on Convex Optimization (2nd). Yurii Nesterov. Springer, 2018.

CSTC316 Optimization Techniques for Data Science

Prerequisites:No prerequisites for this course

Course Nature: TC

Objectives:

Similar Courses:

Introduction

Foundations of Smooth Optimization

Descent Methods

Gradient Methods

First-Order Methods for Constrained Optimization

Duality and Algorithms

Recognize and formulate convex, nonconvex, and structured optimization problems

Understand the basic theory and algorithms for solving large-scale problems

Gain some programming experience on numerical implementations

Use the material in current or future research

Prerequisites:
No prerequisites for this course